Understanding Aesthetic Evaluation using Deep Learning

A bottleneck in any evolutionary art system is aesthetic evaluation. Many different methods have been proposed to automate the evaluation of aesthetics, including measures of symmetry, coherence, complexity, contrast and grouping. The interactive genetic algorithm (IGA) relies on human-in-the-loop, subjective evaluation of aesthetics, but limits possibilities for large search due to user fatigue and small population sizes. In this paper we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist's computer art dataset, we use dimensionality reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in any generative system. Convolutional Neural Networks trained on the user's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings

Translating Dysphagia Evidence into Practice While Avoiding Pitfalls: Assessing Bias Risk in Tracheostomy Literature.

Critically ill patients who require a tracheostomy often have dysphagia. Widespread practice guidelines have yet to be developed regarding the acute assessment and management of dysphagia in patients with tracheostomy. In order for clinicians to base their practice on the best available evidence, they must first assess the applicable literature and determine its quality. To inform guideline development, our objective was to assess literature quality concerning swallowing following tracheostomy in acute stages of critical illness in adults. Our systematic literature search (published previously) included eight databases, nine gray literature repositories and citation chasing. Using inclusion criteria determined a priori, two reviewers, blinded to each other, conducted an eligibility review of identified citations. Patients with chronic tracheostomy and etiologies including head and/or neck cancer diagnoses were excluded. Four teams of two reviewers each, blinded to each other, assessed quality of included studies using a modified Cochrane Risk of Bias tool (RoB). Disagreements were resolved by consensus. Data were summarized descriptively according to study design and RoB domain. Of 6,396 identified citations, 74 studies met our inclusion criteria. Of those, 71 were observational and three were randomized controlled trials. Across all studies, the majority (> 75%) had low bias risk with: participant blinding, outcome reporting, and operationally defined outcomes. Areas requiring improvement included assessor and study personnel blinding. Prior to translating the literature into practice guidelines, we recommend attention to study quality limitations and its potential impact on study outcomes. For future work, we suggest an iterative approach to knowledge translation

Predominance of subtype 3 among Blastocystis isolates from a major hospital in Singapore

10.1007/s00436-007-0808-0Parasitology Research1024663-67

THE NEURAL BASIS OF THE HEDONIC DIMENSION OF AESTHETIC EXPERIENCE.

In this review, I provide an overall description of the findings obtained from neuroimaging studies aimed at investigating the neural substrates underpinning aesthetic experience when viewing masterpieces of Classical art. In particular, I here argue the idea that aesthetic experience is hallmarked by a hedonic response to the stimuli. This response would be triggered, according to our proposal, by integration of emotional and cognitive processes at the level of the right insular cortex and, more specifically, of its anterodorsal sector. I then discuss the recent results suggesting that the hedonic quality characterizing aesthetic experience for artworks is absent when aesthetically appraising biological non-artist stimuli